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4. Pandas进阶使用

4. Pandas进阶使用

作者: archmagus | 来源:发表于2020-06-29 22:48 被阅读0次
    import pandas as pd
    import numpy as np
    
    

    示例 股票数据离散化

    stock=pd.read_csv("/home/python/练习/stock_day/stock_day.csv")
    stock.head()
    
    
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    open high close low volume price_change p_change ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 turnover
    2018-02-27 23.53 25.88 24.16 23.53 95578.03 0.63 2.68 22.942 22.142 22.875 53782.64 46738.65 55576.11 2.39
    2018-02-26 22.80 23.78 23.53 22.80 60985.11 0.69 3.02 22.406 21.955 22.942 40827.52 42736.34 56007.50 1.53
    2018-02-23 22.88 23.37 22.82 22.71 52914.01 0.54 2.42 21.938 21.929 23.022 35119.58 41871.97 56372.85 1.32
    2018-02-22 22.25 22.76 22.28 22.02 36105.01 0.36 1.64 21.446 21.909 23.137 35397.58 39904.78 60149.60 0.90
    2018-02-14 21.49 21.99 21.92 21.48 23331.04 0.44 2.05 21.366 21.923 23.253 33590.21 42935.74 61716.11 0.58
    qcut=pd.qcut(stock["p_change"], 10)
    
    
    qcut.value_counts
    
    
    <bound method IndexOpsMixin.value_counts of 2018-02-27                   (1.738, 2.938]
    2018-02-26                    (2.938, 5.27]
    2018-02-23                   (1.738, 2.938]
    2018-02-22                    (0.94, 1.738]
    2018-02-14                   (1.738, 2.938]
    2018-02-13                    (0.94, 1.738]
    2018-02-12                    (2.938, 5.27]
    2018-02-09    (-10.030999999999999, -4.836]
    2018-02-08                     (0.26, 0.94]
    2018-02-07                 (-2.444, -1.352]
    2018-02-06                 (-4.836, -2.444]
    2018-02-05                   (1.738, 2.938]
    2018-02-02                     (0.26, 0.94]
    2018-02-01    (-10.030999999999999, -4.836]
    2018-01-31                   (-0.462, 0.26]
    2018-01-30                   (-0.462, 0.26]
    2018-01-29                 (-4.836, -2.444]
    2018-01-26                     (0.26, 0.94]
    2018-01-25                 (-4.836, -2.444]
    2018-01-24                 (-1.352, -0.462]
    2018-01-23                    (0.94, 1.738]
    2018-01-22                   (-0.462, 0.26]
    2018-01-19                   (1.738, 2.938]
    2018-01-18                   (-0.462, 0.26]
    2018-01-17                     (0.26, 0.94]
    2018-01-16                    (2.938, 5.27]
    2018-01-15                 (-4.836, -2.444]
    2018-01-12                   (1.738, 2.938]
    2018-01-11                 (-1.352, -0.462]
    2018-01-10                 (-1.352, -0.462]
                              ...              
    2015-04-13                    (5.27, 10.03]
    2015-04-10                 (-1.352, -0.462]
    2015-04-09                    (5.27, 10.03]
    2015-04-08                    (2.938, 5.27]
    2015-04-07                    (5.27, 10.03]
    2015-04-03                    (0.94, 1.738]
    2015-04-02                     (0.26, 0.94]
    2015-04-01                     (0.26, 0.94]
    2015-03-31                 (-2.444, -1.352]
    2015-03-30                    (2.938, 5.27]
    2015-03-27                    (5.27, 10.03]
    2015-03-26                 (-2.444, -1.352]
    2015-03-25                 (-2.444, -1.352]
    2015-03-24                   (1.738, 2.938]
    2015-03-23                   (-0.462, 0.26]
    2015-03-20                   (-0.462, 0.26]
    2015-03-19                     (0.26, 0.94]
    2015-03-18                     (0.26, 0.94]
    2015-03-17                   (1.738, 2.938]
    2015-03-16                   (1.738, 2.938]
    2015-03-13                   (1.738, 2.938]
    2015-03-12                 (-1.352, -0.462]
    2015-03-11                 (-2.444, -1.352]
    2015-03-10                   (1.738, 2.938]
    2015-03-09                   (-0.462, 0.26]
    2015-03-06                    (5.27, 10.03]
    2015-03-05                   (1.738, 2.938]
    2015-03-04                    (0.94, 1.738]
    2015-03-03                    (0.94, 1.738]
    2015-03-02                   (1.738, 2.938]
    Name: p_change, Length: 643, dtype: category
    Categories (10, interval[float64]): [(-10.030999999999999, -4.836] < (-4.836, -2.444] < (-2.444, -1.352] < (-1.352, -0.462] ... (0.94, 1.738] < (1.738, 2.938] < (2.938, 5.27] < (5.27, 10.03]]>
    
    
    pd.get_dummies(qcut,prefix="rise")
    
    
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    rise_(-10.030999999999999, -4.836] rise_(-4.836, -2.444] rise_(-2.444, -1.352] rise_(-1.352, -0.462] rise_(-0.462, 0.26] rise_(0.26, 0.94] rise_(0.94, 1.738] rise_(1.738, 2.938] rise_(2.938, 5.27] rise_(5.27, 10.03]
    2018-02-27 0 0 0 0 0 0 0 1 0 0
    2018-02-26 0 0 0 0 0 0 0 0 1 0
    2018-02-23 0 0 0 0 0 0 0 1 0 0
    2018-02-22 0 0 0 0 0 0 1 0 0 0
    2018-02-14 0 0 0 0 0 0 0 1 0 0
    2018-02-13 0 0 0 0 0 0 1 0 0 0
    2018-02-12 0 0 0 0 0 0 0 0 1 0
    2018-02-09 1 0 0 0 0 0 0 0 0 0
    2018-02-08 0 0 0 0 0 1 0 0 0 0
    2018-02-07 0 0 1 0 0 0 0 0 0 0
    2018-02-06 0 1 0 0 0 0 0 0 0 0
    2018-02-05 0 0 0 0 0 0 0 1 0 0
    2018-02-02 0 0 0 0 0 1 0 0 0 0
    2018-02-01 1 0 0 0 0 0 0 0 0 0
    2018-01-31 0 0 0 0 1 0 0 0 0 0
    2018-01-30 0 0 0 0 1 0 0 0 0 0
    2018-01-29 0 1 0 0 0 0 0 0 0 0
    2018-01-26 0 0 0 0 0 1 0 0 0 0
    2018-01-25 0 1 0 0 0 0 0 0 0 0
    2018-01-24 0 0 0 1 0 0 0 0 0 0
    2018-01-23 0 0 0 0 0 0 1 0 0 0
    2018-01-22 0 0 0 0 1 0 0 0 0 0
    2018-01-19 0 0 0 0 0 0 0 1 0 0
    2018-01-18 0 0 0 0 1 0 0 0 0 0
    2018-01-17 0 0 0 0 0 1 0 0 0 0
    2018-01-16 0 0 0 0 0 0 0 0 1 0
    2018-01-15 0 1 0 0 0 0 0 0 0 0
    2018-01-12 0 0 0 0 0 0 0 1 0 0
    2018-01-11 0 0 0 1 0 0 0 0 0 0
    2018-01-10 0 0 0 1 0 0 0 0 0 0
    ... ... ... ... ... ... ... ... ... ... ...
    2015-04-13 0 0 0 0 0 0 0 0 0 1
    2015-04-10 0 0 0 1 0 0 0 0 0 0
    2015-04-09 0 0 0 0 0 0 0 0 0 1
    2015-04-08 0 0 0 0 0 0 0 0 1 0
    2015-04-07 0 0 0 0 0 0 0 0 0 1
    2015-04-03 0 0 0 0 0 0 1 0 0 0
    2015-04-02 0 0 0 0 0 1 0 0 0 0
    2015-04-01 0 0 0 0 0 1 0 0 0 0
    2015-03-31 0 0 1 0 0 0 0 0 0 0
    2015-03-30 0 0 0 0 0 0 0 0 1 0
    2015-03-27 0 0 0 0 0 0 0 0 0 1
    2015-03-26 0 0 1 0 0 0 0 0 0 0
    2015-03-25 0 0 1 0 0 0 0 0 0 0
    2015-03-24 0 0 0 0 0 0 0 1 0 0
    2015-03-23 0 0 0 0 1 0 0 0 0 0
    2015-03-20 0 0 0 0 1 0 0 0 0 0
    2015-03-19 0 0 0 0 0 1 0 0 0 0
    2015-03-18 0 0 0 0 0 1 0 0 0 0
    2015-03-17 0 0 0 0 0 0 0 1 0 0
    2015-03-16 0 0 0 0 0 0 0 1 0 0
    2015-03-13 0 0 0 0 0 0 0 1 0 0
    2015-03-12 0 0 0 1 0 0 0 0 0 0
    2015-03-11 0 0 1 0 0 0 0 0 0 0
    2015-03-10 0 0 0 0 0 0 0 1 0 0
    2015-03-09 0 0 0 0 1 0 0 0 0 0
    2015-03-06 0 0 0 0 0 0 0 0 0 1
    2015-03-05 0 0 0 0 0 0 0 1 0 0
    2015-03-04 0 0 0 0 0 0 1 0 0 0
    2015-03-03 0 0 0 0 0 0 1 0 0 0
    2015-03-02 0 0 0 0 0 0 0 1 0 0

    643 rows × 10 columns

    bins=[-100,-7,-5,-3,0,3,5,7,100]
    cut=pd.cut(stock['p_change'], bins)
    cut.value_counts
    
    
    <bound method IndexOpsMixin.value_counts of 2018-02-27      (0, 3]
    2018-02-26      (3, 5]
    2018-02-23      (0, 3]
    2018-02-22      (0, 3]
    2018-02-14      (0, 3]
    2018-02-13      (0, 3]
    2018-02-12      (3, 5]
    2018-02-09    (-7, -5]
    2018-02-08      (0, 3]
    2018-02-07     (-3, 0]
    2018-02-06    (-5, -3]
    2018-02-05      (0, 3]
    2018-02-02      (0, 3]
    2018-02-01    (-7, -5]
    2018-01-31     (-3, 0]
    2018-01-30      (0, 3]
    2018-01-29     (-3, 0]
    2018-01-26      (0, 3]
    2018-01-25    (-5, -3]
    2018-01-24     (-3, 0]
    2018-01-23      (0, 3]
    2018-01-22     (-3, 0]
    2018-01-19      (0, 3]
    2018-01-18      (0, 3]
    2018-01-17      (0, 3]
    2018-01-16      (3, 5]
    2018-01-15    (-5, -3]
    2018-01-12      (0, 3]
    2018-01-11     (-3, 0]
    2018-01-10     (-3, 0]
                    ...   
    2015-04-13    (7, 100]
    2015-04-10     (-3, 0]
    2015-04-09      (5, 7]
    2015-04-08      (5, 7]
    2015-04-07      (5, 7]
    2015-04-03      (0, 3]
    2015-04-02      (0, 3]
    2015-04-01      (0, 3]
    2015-03-31     (-3, 0]
    2015-03-30      (3, 5]
    2015-03-27      (5, 7]
    2015-03-26     (-3, 0]
    2015-03-25     (-3, 0]
    2015-03-24      (0, 3]
    2015-03-23      (0, 3]
    2015-03-20     (-3, 0]
    2015-03-19      (0, 3]
    2015-03-18      (0, 3]
    2015-03-17      (0, 3]
    2015-03-16      (0, 3]
    2015-03-13      (0, 3]
    2015-03-12     (-3, 0]
    2015-03-11     (-3, 0]
    2015-03-10      (0, 3]
    2015-03-09      (0, 3]
    2015-03-06    (7, 100]
    2015-03-05      (0, 3]
    2015-03-04      (0, 3]
    2015-03-03      (0, 3]
    2015-03-02      (0, 3]
    Name: p_change, Length: 643, dtype: category
    Categories (8, interval[int64]): [(-100, -7] < (-7, -5] < (-5, -3] < (-3, 0] < (0, 3] < (3, 5] < (5, 7] < (7, 100]]>
    
    
    one_hot=pd.get_dummies(qcut,prefix="涨跌幅").head()
    one_hot
    
    
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    涨跌幅_(-10.030999999999999, -4.836] 涨跌幅_(-4.836, -2.444] 涨跌幅_(-2.444, -1.352] 涨跌幅_(-1.352, -0.462] 涨跌幅_(-0.462, 0.26] 涨跌幅_(0.26, 0.94] 涨跌幅_(0.94, 1.738] 涨跌幅_(1.738, 2.938] 涨跌幅_(2.938, 5.27] 涨跌幅_(5.27, 10.03]
    2018-02-27 0 0 0 0 0 0 0 1 0 0
    2018-02-26 0 0 0 0 0 0 0 0 1 0
    2018-02-23 0 0 0 0 0 0 0 1 0 0
    2018-02-22 0 0 0 0 0 0 1 0 0 0
    2018-02-14 0 0 0 0 0 0 0 1 0 0
    # 数据的合并
    pd.concat([stock,one_hot],axis=1).head()
    
    
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    open high close low volume price_change p_change ma5 ma10 ma20 ... 涨跌幅_(-10.030999999999999, -4.836] 涨跌幅_(-4.836, -2.444] 涨跌幅_(-2.444, -1.352] 涨跌幅_(-1.352, -0.462] 涨跌幅_(-0.462, 0.26] 涨跌幅_(0.26, 0.94] 涨跌幅_(0.94, 1.738] 涨跌幅_(1.738, 2.938] 涨跌幅_(2.938, 5.27] 涨跌幅_(5.27, 10.03]
    2015-03-02 12.25 12.67 12.52 12.20 96291.73 0.32 2.62 12.520 12.520 12.520 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
    2015-03-03 12.52 13.06 12.70 12.52 139071.61 0.18 1.44 12.610 12.610 12.610 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
    2015-03-04 12.80 12.92 12.90 12.61 67075.44 0.20 1.57 12.707 12.707 12.707 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
    2015-03-05 12.88 13.45 13.16 12.87 93180.39 0.26 2.02 12.820 12.820 12.820 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
    2015-03-06 13.17 14.48 14.28 13.13 179831.72 1.12 8.51 13.112 13.112 13.112 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

    5 rows × 24 columns

    stock
    
    
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    open high close low volume price_change p_change ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 turnover
    2018-02-27 23.53 25.88 24.16 23.53 95578.03 0.63 2.68 22.942 22.142 22.875 53782.64 46738.65 55576.11 2.39
    2018-02-26 22.80 23.78 23.53 22.80 60985.11 0.69 3.02 22.406 21.955 22.942 40827.52 42736.34 56007.50 1.53
    2018-02-23 22.88 23.37 22.82 22.71 52914.01 0.54 2.42 21.938 21.929 23.022 35119.58 41871.97 56372.85 1.32
    2018-02-22 22.25 22.76 22.28 22.02 36105.01 0.36 1.64 21.446 21.909 23.137 35397.58 39904.78 60149.60 0.90
    2018-02-14 21.49 21.99 21.92 21.48 23331.04 0.44 2.05 21.366 21.923 23.253 33590.21 42935.74 61716.11 0.58
    2018-02-13 21.40 21.90 21.48 21.31 30802.45 0.28 1.32 21.342 22.103 23.387 39694.65 45518.14 65161.68 0.77
    2018-02-12 20.70 21.40 21.19 20.63 32445.39 0.82 4.03 21.504 22.338 23.533 44645.16 45679.94 68686.33 0.81
    2018-02-09 21.20 21.46 20.36 20.19 54304.01 -1.50 -6.86 21.920 22.596 23.645 48624.36 48982.38 70552.47 1.36
    2018-02-08 21.79 22.09 21.88 21.75 27068.16 0.09 0.41 22.372 23.009 23.839 44411.98 48612.16 73852.45 0.68
    2018-02-07 22.69 23.11 21.80 21.29 53853.25 -0.50 -2.24 22.480 23.258 23.929 52281.28 56315.11 74925.33 1.35
    2018-02-06 22.80 23.55 22.29 22.20 55555.00 -0.97 -4.17 22.864 23.607 24.029 51341.63 64413.58 75738.95 1.39
    2018-02-05 22.45 23.39 23.27 22.25 52341.39 0.65 2.87 23.172 23.928 24.112 46714.72 69278.66 77070.00 1.31
    2018-02-02 22.40 22.70 22.62 21.53 33242.11 0.20 0.89 23.272 24.114 24.184 49340.40 70873.73 79929.71 0.83
    2018-02-01 23.71 23.86 22.42 22.22 66414.64 -1.30 -5.48 23.646 24.365 24.279 52812.35 80394.43 88480.92 1.66
    2018-01-31 23.85 23.98 23.72 23.31 49155.02 -0.11 -0.46 24.036 24.583 24.411 60348.94 80496.48 91666.75 1.23
    2018-01-30 23.71 24.08 23.83 23.70 32420.43 0.05 0.21 24.350 24.671 24.365 77485.53 84805.23 92943.35 0.81
    2018-01-29 24.40 24.63 23.77 23.72 65469.81 -0.73 -2.98 24.684 24.728 24.294 91842.60 91692.73 93456.22 1.64
    2018-01-26 24.27 24.74 24.49 24.22 50601.83 0.11 0.45 24.956 24.694 24.221 92407.05 92122.56 91980.51 1.27
    2018-01-25 24.99 24.99 24.37 24.23 104097.59 -0.93 -3.68 25.084 24.669 24.109 107976.51 99092.73 92262.67 2.61
    2018-01-24 25.49 26.28 25.29 25.20 134838.00 -0.20 -0.79 25.130 24.599 23.997 100644.02 93535.55 89522.22 3.37
    2018-01-23 25.15 25.53 25.50 24.93 104205.76 0.39 1.55 24.992 24.450 23.844 92124.92 87064.33 85876.80 2.61
    2018-01-22 25.14 25.40 25.13 24.75 68292.08 -0.01 -0.04 24.772 24.296 23.644 91542.85 84861.33 84970.00 1.71
    2018-01-19 24.60 25.34 25.13 24.42 128449.11 0.53 2.15 24.432 24.254 23.537 91838.07 88985.70 82975.10 3.21
    2018-01-18 24.40 24.88 24.60 24.30 67435.14 0.01 0.04 24.254 24.192 23.441 90208.95 96567.41 78252.92 1.69
    2018-01-17 24.42 24.92 24.60 23.80 92242.51 0.20 0.82 24.068 24.239 23.378 86427.08 102837.01 77049.61 2.31
    2018-01-16 23.40 24.60 24.40 23.30 101295.42 0.96 4.10 23.908 24.058 23.321 82003.73 101081.47 74590.92 2.54
    2018-01-15 24.01 24.23 23.43 23.30 69768.17 -0.80 -3.30 23.820 23.860 23.257 78179.81 95219.71 71006.65 1.75
    2018-01-12 23.70 25.15 24.24 23.42 120303.53 0.56 2.37 24.076 23.748 23.236 86133.33 91838.46 69690.35 3.01
    2018-01-11 23.67 23.85 23.67 23.21 48525.75 -0.12 -0.50 24.130 23.548 23.197 102925.87 85432.61 65928.23 1.21
    2018-01-10 24.10 24.60 23.80 23.40 70125.79 -0.14 -0.58 24.410 23.394 23.204 119246.95 85508.89 66934.89 1.76
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    2015-04-13 19.60 21.30 21.13 19.50 171822.69 1.70 8.75 19.228 17.812 16.563 149620.34 114456.84 111752.31 5.88
    2015-04-10 19.55 19.89 19.43 19.20 112962.15 -0.19 -0.97 18.334 17.276 16.230 133648.38 109309.78 106228.29 3.87
    2015-04-09 18.28 19.89 19.62 18.02 183119.05 1.20 6.51 17.736 16.826 15.964 124323.21 106501.34 104829.10 6.27
    2015-04-08 17.60 18.53 18.42 17.60 157725.97 0.88 5.02 17.070 16.394 15.698 101421.29 97906.88 101658.57 5.40
    2015-04-07 16.54 17.98 17.54 16.50 122471.85 0.88 5.28 16.620 16.120 15.510 86769.62 97473.29 98832.94 4.19
    2015-04-03 16.44 16.77 16.66 16.25 91962.88 0.22 1.34 16.396 15.904 15.348 79293.34 94172.24 99956.63 3.15
    2015-04-02 16.21 16.50 16.44 16.21 66336.32 0.15 0.92 16.218 15.772 15.229 84971.19 92655.96 104350.08 2.27
    2015-04-01 16.18 16.48 16.29 16.00 68609.42 0.12 0.74 15.916 15.666 15.065 88679.47 95386.75 105692.28 2.35
    2015-03-31 16.78 16.88 16.17 16.07 84467.62 -0.25 -1.52 15.718 15.568 14.896 94392.47 100679.68 105615.58 2.89
    2015-03-30 15.99 16.63 16.42 15.99 85090.45 0.65 4.12 15.620 15.469 14.722 108176.96 108109.99 108345.78 2.91
    2015-03-27 14.90 15.86 15.77 14.90 120352.13 0.84 5.63 15.412 15.314 14.527 109051.14 109047.78 108905.84 4.12
    2015-03-26 15.14 15.35 14.93 14.91 84877.75 -0.37 -2.42 15.326 15.184 14.462 100340.74 103146.79 108303.41 2.91
    2015-03-25 15.97 15.97 15.30 15.18 97174.40 -0.38 -2.42 15.416 15.102 14.436 102094.02 103156.85 109604.83 3.33
    2015-03-24 15.38 16.16 15.68 15.28 153390.08 0.30 1.95 15.418 15.002 14.385 106966.89 105410.25 110336.03 5.25
    2015-03-23 15.34 15.56 15.38 15.25 89461.32 0.04 0.26 15.318 14.899 14.304 108043.02 100192.60 107645.16 3.06
    2015-03-20 15.38 15.48 15.34 15.18 76800.13 -0.04 -0.26 15.216 14.792 14.232 109044.42 105741.03 108857.41 2.63
    2015-03-19 15.20 15.64 15.38 15.11 93644.19 0.07 0.46 15.042 14.686 14.153 105952.84 116044.19 111147.22 3.21
    2015-03-18 15.18 15.66 15.31 15.02 121538.71 0.13 0.86 14.788 14.464 14.058 104219.67 115997.81 112493.60 4.16
    2015-03-17 14.90 15.44 15.18 14.63 158770.77 0.31 2.08 14.586 14.223 13.954 103853.62 110551.48 111739.85 5.43
    2015-03-16 14.52 15.05 14.87 14.51 94468.30 0.40 2.76 14.480 13.975 13.843 92342.17 108581.56 107464.31 3.23
    2015-03-13 14.13 14.50 14.47 14.08 61342.22 0.36 2.55 14.368 13.740 13.740 102437.64 108763.91 108763.91 2.10
    2015-03-12 14.11 14.80 14.11 13.95 84978.37 -0.19 -1.33 14.330 13.659 13.659 126135.54 114032.98 114032.98 2.91
    2015-03-11 14.80 15.08 14.30 14.14 119708.43 -0.35 -2.39 14.140 13.603 13.603 127775.94 117664.81 117664.81 4.10
    2015-03-10 14.20 14.80 14.65 14.01 101213.51 0.34 2.38 13.860 13.503 13.503 117249.34 117372.87 117372.87 3.46
    2015-03-09 14.14 14.85 14.31 13.80 144945.66 0.03 0.21 13.470 13.312 13.312 124820.96 120066.09 120066.09 4.96
    2015-03-06 13.17 14.48 14.28 13.13 179831.72 1.12 8.51 13.112 13.112 13.112 115090.18 115090.18 115090.18 6.16
    2015-03-05 12.88 13.45 13.16 12.87 93180.39 0.26 2.02 12.820 12.820 12.820 98904.79 98904.79 98904.79 3.19
    2015-03-04 12.80 12.92 12.90 12.61 67075.44 0.20 1.57 12.707 12.707 12.707 100812.93 100812.93 100812.93 2.30
    2015-03-03 12.52 13.06 12.70 12.52 139071.61 0.18 1.44 12.610 12.610 12.610 117681.67 117681.67 117681.67 4.76
    2015-03-02 12.25 12.67 12.52 12.20 96291.73 0.32 2.62 12.520 12.520 12.520 96291.73 96291.73 96291.73 3.30

    643 rows × 14 columns

    # 1、先根据对应的日期找到星期几
    date = pd.to_datetime(stock.index).weekday
    stock['week'] = date
    
    # 2、把p_change按照大小分类,以0为界限
    stock['posi_neg'] = np.where(stock['p_change'] > 0, 1, 0)
    
    # 通过交叉表找寻两列数据的关系
    data = pd.crosstab(stock['week'], stock['posi_neg'])
    
    
    data
    
    
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    posi_neg 0 1
    week
    --- --- ---
    0 63 62
    1 55 76
    2 61 71
    3 63 65
    4 59 68
    pona_sum=data.sum(axis=1)
    
    
    pona_sum
    
    
    week
    0    125
    1    131
    2    132
    3    128
    4    127
    dtype: int64
    
    
    data=data.div(pona_sum,axis=0)
    data
    
    
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    posi_neg 0 1
    week
    --- --- ---
    0 0.504000 0.496000
    1 0.419847 0.580153
    2 0.462121 0.537879
    3 0.492188 0.507812
    4 0.464567 0.535433
    data.plot(kind="bar",stacked=True)
    
    
    <matplotlib.axes._subplots.AxesSubplot at 0x7f8cd40efeb8>
    
    
    # 透视表实现更加简单
     stock.pivot_table(["posi_neg"],index=["week"])
    
    
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    posi_neg
    week
    --- ---
    0 0.496000
    1 0.580153
    2 0.537879
    3 0.507812
    4 0.535433
    # 分组
    col =pd.DataFrame({'color': ['white','red','green','red','green'], 'object': ['pen','pencil','pencil','ashtray','pen'],'price1':[5.56,4.20,1.30,0.56,2.75],'price2':[4.75,4.12,1.60,0.75,3.15]})
    
    
    col
    
    
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    color object price1 price2
    0 white pen 5.56 4.75
    1 red pencil 4.20 4.12
    2 green pencil 1.30 1.60
    3 red ashtray 0.56 0.75
    4 green pen 2.75 3.15
    col.groupby(['color'])['price1'].mean()
    
    
    color
    green    2.025
    red      2.380
    white    5.560
    Name: price1, dtype: float64
    
    
    col.groupby(['color'], as_index=False)['price1'].mean()
    
    
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    color price1
    0 green 2.025
    1 red 2.380
    2 white 5.560

    综合示例

    movie=pd.read_csv("/home/python/练习/IMDB/IMDB-Movie-Data.csv")
    
    
    movie.head()
    
    
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    Rank Title Genre Description Director Actors Year Runtime (Minutes) Rating Votes Revenue (Millions) Metascore
    0 1 Guardians of the Galaxy Action,Adventure,Sci-Fi A group of intergalactic criminals are forced ... James Gunn Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... 2014 121 8.1 757074 333.13 76.0
    1 2 Prometheus Adventure,Mystery,Sci-Fi Following clues to the origin of mankind, a te... Ridley Scott Noomi Rapace, Logan Marshall-Green, Michael Fa... 2012 124 7.0 485820 126.46 65.0
    2 3 Split Horror,Thriller Three girls are kidnapped by a man with a diag... M. Night Shyamalan James McAvoy, Anya Taylor-Joy, Haley Lu Richar... 2016 117 7.3 157606 138.12 62.0
    3 4 Sing Animation,Comedy,Family In a city of humanoid animals, a hustling thea... Christophe Lourdelet Matthew McConaughey,Reese Witherspoon, Seth Ma... 2016 108 7.2 60545 270.32 59.0
    4 5 Suicide Squad Action,Adventure,Fantasy A secret government agency recruits some of th... David Ayer Will Smith, Jared Leto, Margot Robbie, Viola D... 2016 123 6.2 393727 325.02 40.0
    # 想知道这些电影数据中评分的平均分,导演的人数等信息,我们应该怎么获取?
    
    
    mean=movie["Rating"].mean()
    print("电影平均分为{}".format(mean))
    
    
    电影平均分为6.723199999999999
    
    
    director_count=movie["Director"].unique().size
    print("共有{}名导演".format(director_count))
    
    
    共有644名导演
    
    
    # 2 对于这一组电影数据,如果我们想看Rating,Runtime (Minutes)的分布情况,应该如何呈现数据?
    
    
    movie["Rating"].plot(kind="hist",figsize=(20,8),bins=20)
    
    
    <matplotlib.axes._subplots.AxesSubplot at 0x7f8cd1b40b38>
    
    
    import matplotlib.pyplot as plt
    plt.figure(figsize=(20,8),dpi=80)
    plt.hist(movie["Rating"].values,bins=20)
     #求出最大最小值
    max_ = movie["Rating"].max()
    min_ = movie["Rating"].min()
    
    # 生成刻度列表
    t1 = np.linspace(min_,max_,num=21)
    
    # [ 1.9    2.255  2.61   2.965  3.32   3.675  4.03   4.385  4.74   5.095  5.45   5.805  6.16   6.515  6.87   7.225  7.58   7.935  8.29   8.645  9\.   ]
    
    # 修改刻度
    plt.xticks(np.linspace(min_,max_,num=21))
    
    # 添加网格
    plt.grid()
    
    plt.show()
    
    
    # 3 对于这一组电影数据,如果我们希望统计电影分类(genre)的情况,应该如何处理数据?
    
    
    movie.head()
    
    
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    Rank Title Genre Description Director Actors Year Runtime (Minutes) Rating Votes Revenue (Millions) Metascore
    0 1 Guardians of the Galaxy Action,Adventure,Sci-Fi A group of intergalactic criminals are forced ... James Gunn Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... 2014 121 8.1 757074 333.13 76.0
    1 2 Prometheus Adventure,Mystery,Sci-Fi Following clues to the origin of mankind, a te... Ridley Scott Noomi Rapace, Logan Marshall-Green, Michael Fa... 2012 124 7.0 485820 126.46 65.0
    2 3 Split Horror,Thriller Three girls are kidnapped by a man with a diag... M. Night Shyamalan James McAvoy, Anya Taylor-Joy, Haley Lu Richar... 2016 117 7.3 157606 138.12 62.0
    3 4 Sing Animation,Comedy,Family In a city of humanoid animals, a hustling thea... Christophe Lourdelet Matthew McConaughey,Reese Witherspoon, Seth Ma... 2016 108 7.2 60545 270.32 59.0
    4 5 Suicide Squad Action,Adventure,Fantasy A secret government agency recruits some of th... David Ayer Will Smith, Jared Leto, Margot Robbie, Viola D... 2016 123 6.2 393727 325.02 40.0
    # 先获取类别的清单
    class_list=[i.split(",")for i in movie["Genre"]]
    
    
    class_list
    
    
    [['Action', 'Adventure', 'Sci-Fi'],
     ['Adventure', 'Mystery', 'Sci-Fi'],
     ['Horror', 'Thriller'],
     ['Animation', 'Comedy', 'Family'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Comedy', 'Drama', 'Music'],
     ['Comedy'],
     ['Action', 'Adventure', 'Biography'],
     ['Adventure', 'Drama', 'Romance'],
     ['Adventure', 'Family', 'Fantasy'],
     ['Biography', 'Drama', 'History'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Animation', 'Adventure', 'Comedy'],
     ['Action', 'Comedy', 'Drama'],
     ['Animation', 'Adventure', 'Comedy'],
     ['Biography', 'Drama', 'History'],
     ['Action', 'Thriller'],
     ['Biography', 'Drama'],
     ['Drama', 'Mystery', 'Sci-Fi'],
     ['Adventure', 'Drama', 'Thriller'],
     ['Drama'],
     ['Crime', 'Drama', 'Horror'],
     ['Animation', 'Adventure', 'Comedy'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Comedy'],
     ['Action', 'Adventure', 'Drama'],
     ['Horror', 'Thriller'],
     ['Comedy'],
     ['Action', 'Adventure', 'Drama'],
     ['Comedy'],
     ['Drama', 'Thriller'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Action', 'Adventure', 'Comedy'],
     ['Action', 'Horror', 'Sci-Fi'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Adventure', 'Drama', 'Sci-Fi'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Action', 'Adventure', 'Western'],
     ['Comedy', 'Drama'],
     ['Animation', 'Adventure', 'Comedy'],
     ['Drama'],
     ['Horror'],
     ['Biography', 'Drama', 'History'],
     ['Drama'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Drama', 'Thriller'],
     ['Adventure', 'Drama', 'Fantasy'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Drama'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Comedy', 'Drama'],
     ['Action', 'Crime', 'Thriller'],
     ['Action', 'Crime', 'Drama'],
     ['Adventure', 'Drama', 'History'],
     ['Crime', 'Horror', 'Thriller'],
     ['Drama', 'Romance'],
     ['Comedy', 'Drama', 'Romance'],
     ['Biography', 'Drama'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Horror', 'Mystery', 'Thriller'],
     ['Crime', 'Drama', 'Mystery'],
     ['Drama', 'Romance', 'Thriller'],
     ['Drama', 'Mystery', 'Sci-Fi'],
     ['Action', 'Adventure', 'Comedy'],
     ['Drama', 'History', 'Thriller'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Drama'],
     ['Action', 'Drama', 'Thriller'],
     ['Drama', 'History'],
     ['Action', 'Drama', 'Romance'],
     ['Drama', 'Fantasy'],
     ['Drama', 'Romance'],
     ['Animation', 'Adventure', 'Comedy'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Action', 'Sci-Fi'],
     ['Adventure', 'Drama', 'War'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Action', 'Comedy', 'Fantasy'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Comedy', 'Drama'],
     ['Biography', 'Comedy', 'Crime'],
     ['Crime', 'Drama', 'Mystery'],
     ['Action', 'Crime', 'Thriller'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Crime', 'Drama'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Crime', 'Drama', 'Mystery'],
     ['Action', 'Crime', 'Drama'],
     ['Crime', 'Drama', 'Mystery'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Drama'],
     ['Comedy', 'Crime', 'Drama'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Action', 'Comedy', 'Crime'],
     ['Animation', 'Drama', 'Fantasy'],
     ['Horror', 'Mystery', 'Sci-Fi'],
     ['Drama', 'Mystery', 'Thriller'],
     ['Crime', 'Drama', 'Thriller'],
     ['Biography', 'Crime', 'Drama'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Adventure', 'Drama', 'Sci-Fi'],
     ['Crime', 'Mystery', 'Thriller'],
     ['Action', 'Adventure', 'Comedy'],
     ['Crime', 'Drama', 'Thriller'],
     ['Comedy'],
     ['Action', 'Adventure', 'Drama'],
     ['Drama'],
     ['Drama', 'Mystery', 'Sci-Fi'],
     ['Action', 'Horror', 'Thriller'],
     ['Biography', 'Drama', 'History'],
     ['Romance', 'Sci-Fi'],
     ['Action', 'Fantasy', 'War'],
     ['Adventure', 'Drama', 'Fantasy'],
     ['Comedy'],
     ['Horror', 'Thriller'],
     ['Action', 'Biography', 'Drama'],
     ['Drama', 'Horror', 'Mystery'],
     ['Animation', 'Adventure', 'Comedy'],
     ['Adventure', 'Drama', 'Family'],
     ['Adventure', 'Mystery', 'Sci-Fi'],
     ['Adventure', 'Comedy', 'Romance'],
     ['Action'],
     ['Action', 'Thriller'],
     ['Adventure', 'Drama', 'Family'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Adventure', 'Crime', 'Mystery'],
     ['Comedy', 'Family', 'Musical'],
     ['Adventure', 'Drama', 'Thriller'],
     ['Drama'],
     ['Adventure', 'Comedy', 'Drama'],
     ['Drama', 'Horror', 'Thriller'],
     ['Drama', 'Music'],
     ['Action', 'Crime', 'Thriller'],
     ['Crime', 'Drama', 'Thriller'],
     ['Crime', 'Drama', 'Thriller'],
     ['Drama', 'Romance'],
     ['Mystery', 'Thriller'],
     ['Mystery', 'Thriller', 'Western'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Comedy', 'Family'],
     ['Biography', 'Comedy', 'Drama'],
     ['Drama'],
     ['Drama', 'Western'],
     ['Drama', 'Mystery', 'Romance'],
     ['Comedy', 'Drama'],
     ['Action', 'Drama', 'Mystery'],
     ['Comedy'],
     ['Action', 'Adventure', 'Crime'],
     ['Adventure', 'Family', 'Fantasy'],
     ['Adventure', 'Sci-Fi', 'Thriller'],
     ['Drama'],
     ['Action', 'Crime', 'Drama'],
     ['Drama', 'Horror', 'Mystery'],
     ['Action', 'Horror', 'Sci-Fi'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Comedy', 'Drama', 'Romance'],
     ['Action', 'Comedy', 'Fantasy'],
     ['Action', 'Comedy', 'Mystery'],
     ['Thriller', 'War'],
     ['Action', 'Comedy', 'Crime'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Action', 'Adventure', 'Crime'],
     ['Action', 'Adventure', 'Thriller'],
     ['Drama', 'Fantasy', 'Romance'],
     ['Action', 'Adventure', 'Comedy'],
     ['Biography', 'Drama', 'History'],
     ['Action', 'Drama', 'History'],
     ['Action', 'Adventure', 'Thriller'],
     ['Crime', 'Drama', 'Thriller'],
     ['Animation', 'Adventure', 'Family'],
     ['Adventure', 'Horror'],
     ['Drama', 'Romance', 'Sci-Fi'],
     ['Animation', 'Adventure', 'Comedy'],
     ['Action', 'Adventure', 'Family'],
     ['Action', 'Adventure', 'Drama'],
     ['Action', 'Comedy'],
     ['Horror', 'Mystery', 'Thriller'],
     ['Action', 'Adventure', 'Comedy'],
     ['Comedy', 'Romance'],
     ['Horror', 'Mystery'],
     ['Drama', 'Family', 'Fantasy'],
     ['Sci-Fi'],
     ['Drama', 'Thriller'],
     ['Drama', 'Romance'],
     ['Drama', 'War'],
     ['Drama', 'Fantasy', 'Horror'],
     ['Crime', 'Drama'],
     ['Comedy', 'Drama', 'Romance'],
     ['Drama', 'Romance'],
     ['Drama'],
     ['Crime', 'Drama', 'History'],
     ['Horror', 'Sci-Fi', 'Thriller'],
     ['Action', 'Drama', 'Sport'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Crime', 'Drama', 'Thriller'],
     ['Adventure', 'Biography', 'Drama'],
     ['Biography', 'Drama', 'Thriller'],
     ['Action', 'Comedy', 'Crime'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Drama', 'Fantasy', 'Horror'],
     ['Biography', 'Drama', 'Thriller'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Action', 'Adventure', 'Mystery'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Drama', 'Horror'],
     ['Comedy', 'Drama', 'Romance'],
     ['Comedy', 'Romance'],
     ['Drama', 'Horror', 'Thriller'],
     ['Action', 'Adventure', 'Drama'],
     ['Drama'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Action', 'Drama', 'Mystery'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Action', 'Adventure', 'Comedy'],
     ['Drama', 'Horror'],
     ['Action', 'Comedy'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Animation', 'Adventure', 'Comedy'],
     ['Horror', 'Mystery'],
     ['Crime', 'Drama', 'Mystery'],
     ['Comedy', 'Crime'],
     ['Drama'],
     ['Comedy', 'Drama', 'Romance'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Action', 'Adventure', 'Family'],
     ['Horror', 'Sci-Fi', 'Thriller'],
     ['Drama', 'Fantasy', 'War'],
     ['Crime', 'Drama', 'Thriller'],
     ['Action', 'Adventure', 'Drama'],
     ['Action', 'Adventure', 'Thriller'],
     ['Action', 'Adventure', 'Drama'],
     ['Drama', 'Romance'],
     ['Biography', 'Drama', 'History'],
     ['Drama', 'Horror', 'Thriller'],
     ['Adventure', 'Comedy', 'Drama'],
     ['Action', 'Adventure', 'Romance'],
     ['Action', 'Drama', 'War'],
     ['Animation', 'Adventure', 'Comedy'],
     ['Animation', 'Adventure', 'Comedy'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Adventure', 'Family', 'Fantasy'],
     ['Drama', 'Musical', 'Romance'],
     ['Drama', 'Sci-Fi', 'Thriller'],
     ['Comedy', 'Drama'],
     ['Action', 'Comedy', 'Crime'],
     ['Biography', 'Comedy', 'Drama'],
     ['Comedy', 'Drama', 'Romance'],
     ['Drama', 'Thriller'],
     ['Biography', 'Drama', 'History'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Horror', 'Mystery', 'Thriller'],
     ['Comedy'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Action', 'Drama', 'Sci-Fi'],
     ['Horror'],
     ['Drama', 'Thriller'],
     ['Comedy', 'Drama', 'Romance'],
     ['Drama', 'Thriller'],
     ['Comedy', 'Drama'],
     ['Drama'],
     ['Action', 'Adventure', 'Comedy'],
     ['Drama', 'Horror', 'Thriller'],
     ['Comedy'],
     ['Drama', 'Sci-Fi'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Horror'],
     ['Action', 'Adventure', 'Thriller'],
     ['Adventure', 'Fantasy'],
     ['Action', 'Comedy', 'Crime'],
     ['Comedy', 'Drama', 'Music'],
     ['Animation', 'Adventure', 'Comedy'],
     ['Action', 'Adventure', 'Mystery'],
     ['Action', 'Comedy', 'Crime'],
     ['Crime', 'Drama', 'History'],
     ['Comedy'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Crime', 'Mystery', 'Thriller'],
     ['Action', 'Adventure', 'Crime'],
     ['Thriller'],
     ['Biography', 'Drama', 'Romance'],
     ['Action', 'Adventure'],
     ['Action', 'Fantasy'],
     ['Action', 'Comedy'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Action', 'Comedy', 'Crime'],
     ['Thriller'],
     ['Action', 'Drama', 'Horror'],
     ['Comedy', 'Music', 'Romance'],
     ['Comedy'],
     ['Drama'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Drama', 'Romance'],
     ['Animation', 'Adventure', 'Comedy'],
     ['Comedy', 'Drama'],
     ['Biography', 'Crime', 'Drama'],
     ['Drama', 'History'],
     ['Action', 'Crime', 'Thriller'],
     ['Action', 'Biography', 'Drama'],
     ['Horror'],
     ['Comedy', 'Romance'],
     ['Comedy', 'Romance'],
     ['Comedy', 'Crime', 'Drama'],
     ['Adventure', 'Family', 'Fantasy'],
     ['Crime', 'Drama', 'Thriller'],
     ['Action', 'Crime', 'Thriller'],
     ['Comedy', 'Romance'],
     ['Biography', 'Drama', 'Sport'],
     ['Drama', 'Romance'],
     ['Drama', 'Horror'],
     ['Adventure', 'Fantasy'],
     ['Adventure', 'Family', 'Fantasy'],
     ['Action', 'Drama', 'Sci-Fi'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Action', 'Horror'],
     ['Comedy', 'Horror', 'Thriller'],
     ['Action', 'Crime', 'Thriller'],
     ['Crime', 'Drama', 'Music'],
     ['Drama'],
     ['Action', 'Crime', 'Thriller'],
     ['Action', 'Sci-Fi', 'Thriller'],
     ['Biography', 'Drama'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Drama', 'Horror', 'Sci-Fi'],
     ['Biography', 'Comedy', 'Drama'],
     ['Crime', 'Horror', 'Thriller'],
     ['Crime', 'Drama', 'Mystery'],
     ['Animation', 'Adventure', 'Comedy'],
     ['Action', 'Biography', 'Drama'],
     ['Biography', 'Drama'],
     ['Biography', 'Drama', 'History'],
     ['Action', 'Biography', 'Drama'],
     ['Drama', 'Fantasy', 'Horror'],
     ['Comedy', 'Drama', 'Romance'],
     ['Drama', 'Sport'],
     ['Drama', 'Romance'],
     ['Comedy', 'Romance'],
     ['Action', 'Crime', 'Thriller'],
     ['Action', 'Crime', 'Drama'],
     ['Action', 'Drama', 'Thriller'],
     ['Adventure', 'Family', 'Fantasy'],
     ['Action', 'Adventure'],
     ['Action', 'Adventure', 'Romance'],
     ['Adventure', 'Family', 'Fantasy'],
     ['Crime', 'Drama'],
     ['Comedy', 'Horror'],
     ['Comedy', 'Fantasy', 'Romance'],
     ['Drama'],
     ['Drama'],
     ['Comedy', 'Drama'],
     ['Comedy', 'Drama', 'Romance'],
     ['Adventure', 'Sci-Fi', 'Thriller'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Comedy', 'Drama'],
     ['Biography', 'Drama', 'Romance'],
     ['Comedy', 'Fantasy'],
     ['Comedy', 'Drama', 'Fantasy'],
     ['Comedy'],
     ['Horror', 'Thriller'],
     ['Action', 'Adventure', 'Sci-Fi'],
     ['Adventure', 'Comedy', 'Horror'],
     ['Comedy', 'Mystery'],
     ['Drama'],
     ['Adventure', 'Drama', 'Fantasy'],
     ['Drama', 'Sport'],
     ['Action', 'Adventure'],
     ['Action', 'Adventure', 'Drama'],
     ['Action', 'Drama', 'Sci-Fi'],
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     ['Drama', 'Thriller'],
     ['Biography', 'Drama', 'Family'],
     ['Comedy', 'Drama', 'Family'],
     ['Drama', 'Fantasy', 'Musical'],
     ['Comedy'],
     ['Adventure', 'Family'],
     ['Adventure', 'Comedy', 'Fantasy'],
     ['Horror', 'Thriller'],
     ['Drama', 'Romance'],
     ['Horror'],
     ['Biography', 'Drama', 'History'],
     ['Action', 'Adventure', 'Fantasy'],
     ['Drama', 'Family', 'Music'],
     ['Comedy', 'Drama', 'Romance'],
     ['Action', 'Adventure', 'Horror'],
     ['Comedy'],
     ['Crime', 'Drama', 'Mystery'],
     ['Horror'],
     ['Drama', 'Music', 'Romance'],
     ['Adventure', 'Comedy'],
     ['Comedy', 'Family', 'Fantasy']]
    
    
    genre_list=[j for i in class_list for j in i]
    
    
    # 去重
    genre=np.unique(genre_list)
    genre
    
    
    array(['Action', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime',
           'Drama', 'Family', 'Fantasy', 'History', 'Horror', 'Music',
           'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Sport', 'Thriller',
           'War', 'Western'], dtype='<U9')
    
    
    # 创建一个用于统计电影类别的空的dataframe
    
    
    genre_type=pd.DataFrame(np.zeros((1000,len(genre)),dtype=np.int32), columns=genre)
    genre_type.head()
    
    
    .dataframe thead th {
        text-align: left;
    }
    
    .dataframe tbody tr th {
        vertical-align: top;
    }
    
    
    Action Adventure Animation Biography Comedy Crime Drama Family Fantasy History Horror Music Musical Mystery Romance Sci-Fi Sport Thriller War Western
    0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
    1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
    2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
    3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
    4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
     for i in range(1000):
            genre_type.loc[i,class_list[i]]=1
    
    
    genre_type.head()
    
    
    .dataframe thead th {
        text-align: left;
    }
    
    .dataframe tbody tr th {
        vertical-align: top;
    }
    
    
    Action Adventure Animation Biography Comedy Crime Drama Family Fantasy History Horror Music Musical Mystery Romance Sci-Fi Sport Thriller War Western
    0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
    1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0
    2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0
    3 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
    4 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
    genre_type.sum(axis=0).sort_values(ascending=False). plot(kind="bar",figsize=(20,8),fontsize=30,colormap="cool")
    
    
    <matplotlib.axes._subplots.AxesSubplot at 0x7f8cd01176a0>
    
    

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